Since its founding in 2012, CircleUp has helped 200+ consumer-packaged goods (CPG) companies raise $315 million of capital, carrying an unrealized IRR of more than 40%. Accredited investors can participate in the platform in 3 ways: 1) investing directly into companies pre-screened by CircleUp “Helio”, 2) investing in CircleUp’s Market Index Fund ($25,000 minimum investment, 0.5% management fee), which offers diversified exposure to the universe filtered by Helio, or 3) participate in thematic syndicates where the lead investor gets paid if the syndicate return a profit. Direct investments are managed similarly to crowdfunding platform, where fund is committed but only changes hand if the funding goal is met.
Companies on the platform typically have $1M or more in sales for the current fiscal year and at least $500,000 in the previous 6 months
Big Data => Low End Disruption
The crux of CircleUp’s value proposition is ‘Helio’. In CircleUp’s CEO Ryan Caldbeck’s own words, Helio uses “a web of algorithms and data sets meant to mimic the investor’s mind,” including financial performance, competitors’ performance, brand, distribution, team, gathered from company’s application, social media and third party sources.
It is built on the premise that CPG companies have similar business models, performance metrics don’t differ much, creating opportunities to train machines really well on a set of data. Retail market data, such as inventory, distribution and sales, is also widely available in the public sphere.
CircleUp solves the market failure that small emerging CPG brands are too small for private equity investing and don’t have the same viral growth trajectory that venture capitalists look for. By coming up with a very good algorithm that can predict success in a narrow field, CircleUp can generate quality investment guidance for low cost, opening up access to capital for brands and creating opportunities for investors to support brands they care about with a reasonable return.
This method of “Big Data” investing, compared to the intuition and assessment of highly analytical individuals, arguably is more efficient at processing big data set but come with its own set of challenges. For novel products for which there aren’t appropriate competitors, the data set may be slim and algorithm rendered insignificant. As machines are trained on historical data, it is a bet that history will repeat itself and market conditions aren’t going to change materially enough to affect the outcome of investments. By comparison, an investor can calibrate her outlook for the market with historical performance of a brand and come up with a thesis for success. On the other hand, machines remove some, though not all the biases in investment.
CircleUp takes a commission as a percentage of fund raise in line with investment banking fee for the same size of the round. By this economics, they are betting that attracting the investors first will create more demand for the entrepreneurs. They could also be recognizing that investors have more choices to invest than entrepreneurs have choices for fundraising.
Setting the commission ‘market rate’ is an interesting proposition. If CircleUp really created a market that did not exist before, how would it define this market rate? Assuming the cost of fundraising before CircleUp includes not only broker fee but also time and effort of the entrepreneurs, the value proposition is much stronger.